ASME

Journal of Computing and Information Science in Engineering

Call for Papers: Special Issue on Human-Robot Collaboration in Industry 5.0

Share This Post

Modern manufacturing systems are expected to evolve into highly automated and intelligent systems capable of handling complex tasks. However, certain manual operations persist on the shop floor that are either impractical or unattainable without human involvement. Industry 5.0 has emerged as a practical approach that complements the existing Industry 4.0 paradigm by emphasizing the transition toward sustainable, human-centric, and resilient manufacturing. Human-robot collaboration (HRC) is considered pivotal in this transition, wherein robots collaborate with human partners to automate physically demanding tasks and integrate humans’ high-level cognition and decision-making. Unfortunately, current HRC methods have seen limited deployment in industries, facing challenges related to understanding, communication, building trust between humans and robots, effective interfaces, task division, allocation, and optimization. Recent advancements in areas including AI, computer vision, extended reality, IIoT, advanced sensing, digital twins, and data analytics provide new affordance to address the aforementioned challenges. For this purpose, this special issue seeks to explore and compile the forefront of research and innovation in HRC within the context of Industry 5.0.

Topic Areas

THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

  • Multi-modal natural interactions and interfaces for HRC
  • Augmented/Mixed/Extended reality (AR/MR/XR) for HRC
  • Large Language Model (LLM), Vision Transformers (ViTs), and other large models based multi-modal HRC
  • Human intent understanding, motion analysis, and action prediction
  • Digital twins and model-based methods for HRC 
  • Tasks analysis, division, and optimization for HRC
  • Environment sensing, situational awareness, 3D reconstruction, and perception for HRC
  • Adaptive robot motion planning methods and interfaces
  • Trust building and cognition aspects related to robots
  • Ergonomics/human factors in HRC

Special Issue Publication Dates

Paper submission deadline: June 1, 2024
Initial review completed: August 1, 2024
Publication date: April 2025

Submission Instructions

Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here

Once at the Paper Submittal page, select the Journal of Computing and Information Science in Engineering, and then select the Special Issue on Human-Robot Collaboration in Industry 5.0 .

Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.

Guest Editors

Dr. Chih-Hsing Chu, National Tsing Hua University, Taiwan (chchu@ie.nthu.edu.tw)

Dr. Yunbo “WILL” Zhang, Rochester Institute of Technology, USA (ywzeie@rit.edu)

Dr. Francesco Ferrise, Politecnico di Milano, Italy (francesco.ferrise@polimi.it)

Dr. Pai Zheng, The Hong Kong Polytechnic University, China (pai.zheng@polyu.edu.hk)

Dr. Qing (Cindy) Chang, University of Virginia, USA (qc9nq@virginia.edu)

GO TO ASME DIGITAL COLLECTION

Visit the ASME Digital Collection archives for JCISE

More To Explore

Announcements

Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.

JCISE VIDEOS